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At one extreme we have repetitive, well-defined problems (e.g., quality control or production lot-size problems) involving tangible considerations, to which the economic models that call for finding the best among a set of pre-established alternatives can be applied rather literally. In contrast to these highly programmed and usually rather detailed decisions are problems of a non-repetitive sort, often involving basic long-range questions about the whole strategy of the firm or some part of it, arising initially in a highly unstructured form and requiring a great deal of the kinds of search processes listed above. In this whole continuum, from great specificity and repetition to extreme vagueness and uniqueness, we will call decisions that lie toward the former extreme programmed, and those lying toward the latter end non-programmed. This simple dichotomy is just a shorthand for the range of possibilities we have indicated.This also introduces an interesting additional way to think about the spectrum: The left side is representative of those ideas where you have the most clarity about the final goal (in manufacturing you know exactly what you want the output to look like when it's done) and the right the most ambiguity (the goal of R&D is to make something new). For that reason, high variance tasks should also fail far more often than their low variance counterparts: Nine out of ten new product ideas might be a good batting average, but if you are throwing away 90 percent of your manufactured output you've massively failed. Even though it may be tempting, that's not a reason to focus purely on the well-structured, low-variance problems, as Richard Cyert laid out in a 1994 paper titled "Positioning the Organization":
It is difficult to deal with the uncertainty of the future, as one must to relate an organization to others in the industry and to events in the economy that may affect it. One must look ahead to determine what forces are at work and to examine the ways in which they will affect the organization. These activities are less structured and more ambiguous than dealing with concrete problems and, therefore, the CEO may have trouble focusing on them. Many experiments show that structured activity drives out unstructured. For example, it is much easier to answer one's mail than to develop a plan to change the culture of the organization. The implications of change are uncertain and the planning is unstructured. One tends to avoid uncertainty and to concentrate on structured problems for which one can correctly predict the solutions and implications.2Going a level deeper, another way to cut the left and right sides of the spectrum is based on the most appropriate way to solve the problem. For the routine tasks you want to have a single way of doing things in an attempt to push down the variance of the output while on the high variance side you have much more freedom to try different approaches. In software terms this can be expressed as automation and collaboration respectively. While this is primarily a framework for thinking about process, there's a more personal way to think about the variance spectrum as it relates to giving feedback to others. It's a common occurrence that employees over-or-misinterpret the feedback of more senior members of the team. I experienced this many times myself in my role as CEO. Because words are often taken literally from the leader of a company, an aside about something like color choice in a design comp can be easily misconstrued as an order to change when it wasn't meant that way. The variance spectrum in that context can be used to make explicit where the feedback falls: Is it a low variance order you expect to be acted on or a high variance comment that is simply your two cents? I found this could help avoid ambiguity and also make it more clear I respected their expertise. Footnotes: